• analyses: Find location distribution of squirrels in Central Park in 2018. Analyze the frequency for squirrel sightings by different fur colors. Investigate the relationship of behavior observation and age & fur of squirrels. ANOVA Test/ T-test- whether behavior are associated with age & fur of squirrels Construct data model to predict the behavior/habitat of a specific squirrel

  • visualizations: Interactive map: Relative habitat of the squirrels in Central Park that match the filter Scatter Plot (Smooth): Whether specific behaviors of squirrels generate (increase) trends according to age Plot: Does the combination of squirrel’s fur color produce a certain regularity of habitat or cluster effect?

  • analyses: Find location distribution of squirrels in Central Park in 2018. Analyze the frequency for squirrel sightings by different fur colors. Investigate the relationship of behavior observation and age & fur of squirrels. ANOVA Test/ T-test- whether behavior are associated with age & fur of squirrels Construct data model to predict the behavior/habitat of a specific squirrel

  • visualizations: Interactive map: Relative habitat of the squirrels in Central Park that match the filter Scatter Plot (Smooth): Whether specific behaviors of squirrels generate (increase) trends according to age Plot: Does the combination of squirrel’s fur color produce a certain regularity of habitat or cluster effect?

skimr::skim(census)
Data summary
Name census
Number of rows 3023
Number of columns 16
_______________________
Column type frequency:
character 13
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
unique_squirrel_id 0 1.00 13 14 0 3018 0
hectare 0 1.00 3 3 0 339 0
shift 0 1.00 2 2 0 2 0
date 0 1.00 8 8 0 11 0
age 121 0.96 1 8 0 3 0
primary_fur_color 55 0.98 4 8 0 3 0
highlight_fur_color 1086 0.64 4 22 0 10 0
combination_of_primary_and_highlight_color 0 1.00 1 27 0 22 0
location 64 0.98 12 12 0 2 0
lat_long 0 1.00 38 45 0 3023 0
activity 200 0.93 6 8 0 5 0
reaction 780 0.74 9 11 0 3 0
sounds 2884 0.05 4 5 0 3 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
hectare_squirrel_number 0 1 4.12 3.10 1.00 2.00 3.00 6.00 23.00 ▇▂▁▁▁
long 0 1 -73.97 0.01 -73.98 -73.97 -73.97 -73.96 -73.95 ▅▇▅▆▂
lat 0 1 40.78 0.01 40.76 40.77 40.78 40.79 40.80 ▇▇▃▅▆ 
glimpse(census)
## Rows: 3,023
## Columns: 16
## $ unique_squirrel_id                         <chr> "37F-PM-1014-03", "21B-AM-1…
## $ hectare                                    <chr> "37F", "21B", "11B", "32E",…
## $ shift                                      <chr> "PM", "AM", "PM", "PM", "AM…
## $ date                                       <chr> "10142018", "10192018", "10…
## $ hectare_squirrel_number                    <dbl> 3, 4, 8, 14, 5, 3, 2, 2, 1,…
## $ age                                        <chr> NA, NA, NA, "Adult", "Adult…
## $ primary_fur_color                          <chr> NA, NA, "Gray", "Gray", "Gr…
## $ highlight_fur_color                        <chr> NA, NA, NA, NA, "Cinnamon",…
## $ combination_of_primary_and_highlight_color <chr> "+", "+", "Gray+", "Gray+",…
## $ location                                   <chr> NA, NA, "Above Ground", NA,…
## $ lat_long                                   <chr> "POINT (-73.9561344937861 4…
## $ long                                       <dbl> -73.95613, -73.96886, -73.9…
## $ lat                                        <dbl> 40.79408, 40.78378, 40.7755…
## $ activity                                   <chr> NA, NA, "chasing", "eating"…
## $ reaction                                   <chr> NA, NA, NA, "runs_from", NA…
## $ sounds                                     <chr> NA, NA, NA, NA, NA, NA, NA,…
view(census)
nyc_squirrels1=
  census%>%
  ggplot(aes(long, lat)) +
  geom_point() +
  theme_map()
nyc_squirrels1

nyc_squirrelmap <- leaflet(data=census1) %>%
  addTiles() %>%  
  addMarkers(lng=~long, lat=~lat, popup= ~hectare,clusterOptions = markerClusterOptions())
nyc_squirrelmap
#这个要在shiny的格式里看，我以防万一还是用了markdown
# primary fur color map

pal_coats <- colorFactor(c("#000000", "#D2691E", "#D3D3D3", "white"), domain = c("Black", "Cinnamon", "Grey", "NA"))

#Locations based on fur color
census %>%
  filter(!is.na(primary_fur_color)) %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(lng = ~long,
                   lat = ~lat, radius = 3, color = ~pal_coats(primary_fur_color), stroke = FALSE, fillOpacity = 0.5) %>%
  addLegend(position ="topright",pal = pal_coats, values = ~primary_fur_color)
omitted<-na.omit(census)
omitted%>%
  filter(omitted$age!="?")
## # A tibble: 53 × 16
##    unique_sq…¹ hectare shift date  hecta…² age   prima…³ highl…⁴ combi…⁵ locat…⁶
##    <chr>       <chr>   <chr> <chr>   <dbl> <chr> <chr>   <chr>   <chr>   <chr>  
##  1 16G-AM-101… 16G     AM    1010…       3 Adult Gray    White   Gray+W… Ground…
##  2 4D-PM-1008… 04D     PM    1008…      11 Adult Cinnam… Gray, … Cinnam… Above …
##  3 37H-AM-101… 37H     AM    1018…       3 Adult Gray    White   Gray+W… Above …
##  4 22C-AM-101… 22C     AM    1013…       2 Adult Cinnam… Gray    Cinnam… Above …
##  5 31E-PM-100… 31E     PM    1006…       2 Adult Gray    Cinnam… Gray+C… Ground…
##  6 37H-PM-100… 37H     PM    1006…       2 Adult Gray    Cinnam… Gray+C… Ground…
##  7 42G-PM-101… 42G     PM    1014…       1 Adult Gray    White   Gray+W… Above …
##  8 8A-PM-1014… 08A     PM    1014…       1 Adult Gray    White   Gray+W… Ground…
##  9 36F-AM-101… 36F     AM    1017…       4 Juve… Gray    White   Gray+W… Ground…
## 10 9B-AM-1010… 09B     AM    1010…       1 Adult Gray    Cinnam… Gray+C… Ground…
## # … with 43 more rows, 6 more variables: lat_long <chr>, long <dbl>, lat <dbl>,
## #   activity <chr>, reaction <chr>, sounds <chr>, and abbreviated variable
## #   names ¹​unique_squirrel_id, ²​hectare_squirrel_number, ³​primary_fur_color,
## #   ⁴​highlight_fur_color, ⁵​combination_of_primary_and_highlight_color,
## #   ⁶​location
plot(factor(location) ~ factor(age), data = omitted)

plot(factor(location) ~ factor(shift), data = omitted)

plot(factor(location) ~ factor(activity), data = omitted)

plot(factor(location) ~ factor(reaction), data = omitted)

plot(factor(long) ~ factor(activity), data = omitted)

library(revgeo)
revgeo(longitude=-73.968285, latitude=40.785091, provider = 'photon', output='frame')
## [1] "Getting geocode data from Photon: http://photon.komoot.de/reverse?lon=-73.968285&lat=40.785091"
##              housenumber           street           city           state
## 1 House Number Not Found Street Not Found City Not Found State Not Found
##                  zip           country
## 1 Postcode Not Found Country Not Found
library(rgdal)
d <- data.frame(lon=census$long, lat=census$lat)
coordinates(d) <- c("lon", "lat")
# Plot the results.
par(mfrow=c(1,3))
plot.default(census$long,census$lat, main="Raw data", cex.axis=.95)
plot(d, axes=TRUE, main="Original lat-lon", cex.axis=.95)

#sf <- st_as_sf(d, coords = c('census$long', 'census$lat'))
#ggplot(sf) + 
  #geom_sf( )+
  #geom_point(aes(census$long, census$lat),color="steelblue") +
  #theme_map()